[Abstract]: In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing algorithms may not scale well when dealing with huge datasets, and a possible solution is to distribute the data in several nodes. In this work we explore the different ways of distributing the data (by features and by samples) and we evaluate to what extent it is possible to obtain similar results as those obtained with the whole dataset. Trying to deal with the challenge of distributing the feature ranking process, we have performed experiments with different aggregation methods and feature rankers, and also evaluated the effect of distributing the feature ranking process in the subsequent classification performance.

This research has been economically supported in part by the Spanish Ministerio de Economía y Competitividad and FEDER funds of the European Union through the research project TIN2015-65069-C2-1-R; and by the Consellería de Industria of the Xunta de Galicia through the research project GRC2014/035. Financial support from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016-2019) and the European Union (European Regional Development Fund - ERDF), is gratefully acknowledged (research project ED431G/01). V. Bolón-Canedo acknowledges support of the Xunta de Galicia under postdoctoral Grant code ED481B 2014/164-0.

This version of the article: Bolón-Canedo, V., Sechidis, K., Sánchez-Maroño, N., Alonso-Betanzos, A., & Brown, G. (2019). ‘Insights into distributed feature ranking’ has been accepted for publication in: Information Sciences, 496, 378–398. The Version of Record is available online at https://doi.org/10.1016/j.ins.2018.09.045.

Xunta de Galicia; ED481B 2014/164-0

Xunta de Galicia; GRC2014/035

Xunta de Galicia; ED431G/01